Women with 8 or more of 12 SNPs are 4.5 times more likely to develop the disease.!--h2>

Investigators at The University of Texas M. D. Anderson Cancer Center have found that genetic variations in miRNA processing pathway genes and binding sites predict a woman’s risk for developing ovarian cancer and her prospects for survival.

“We found a gene dosage effect—the more unfavorable variations a woman has, the greater her ovarian cancer risk and the shorter her survival time,” says senior author Xifeng Wu, M.D., Ph.D., professor in M.D. Anderson’s department of epidemiology. The researchers determined that 12 SNPs were significantly associated with ovarian cancer risk. Compared to women with five or fewer unfavorable genotypes, those women with 8 or more were 4.5 times more likely to develop ovarian cancer, while those women with 6 to 8 unfavorable SNPs were at twice the risk.

The team analyzed 219 potential functional SNPs in eight genes that process miRNA and at the miRNA binding sites of 129 cancer-relevant genes. The study examined genetic information from 417 cancer patients and 417 healthy controls. To minimize the possible confounding effects of ethnicity, 339 Caucasian cases and 349 controls were analyzed.

The scientists also found 21 SNPs significantly associated with overall survival. Median survival was 151 months for women with six or fewer unfavorable variations, 42 months for those with seven to nine unfavorable variations, and 24 months for those with 10 or more. One of the outcome risk SNPs also was strongly associated with platinum-based chemotherapy response. Those having the SNP were 3.4 times less likely to respond to chemotherapy according to the research, recently reported at the American Association for Cancer Research.

“Our findings have the potential clinical application of indicating a patient’s prognosis and showing who will respond to different therapies by analyzing a single blood sample,” said Dr. Wu. “We also will incorporate this genetic information with epidemiological information to build a comprehensive model to predict susceptibility to ovarian cancer.”